Neuropeptidergic circuit modulation of developmental sleep in Drosophila

Neuropeptidergic circuit modulation of developmental sleep in Drosophila | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Neuropeptidergic circuit modulation of developmental sleep in Drosophila Chikayo Hemmi , View ORCID Profile Kenichi Ishii , Mana Motoyoshi , Masato Tsuji , Kazuo Emoto doi: https://doi.org/10.1101/2025.01.20.633944 Chikayo Hemmi 1 Department of Biological Sciences, The University of Tokyo Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kenichi Ishii 1 Department of Biological Sciences, The University of Tokyo Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kenichi Ishii For correspondence: ishii-k{at}g.ecc.u-tokyo.ac.jp Mana Motoyoshi 1 Department of Biological Sciences, The University of Tokyo Find this author on Google Scholar Find this author on PubMed Search for this author on this site Masato Tsuji 1 Department of Biological Sciences, The University of Tokyo Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kazuo Emoto 1 Department of Biological Sciences, The University of Tokyo 2 International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ishii-k{at}g.ecc.u-tokyo.ac.jp Abstract Full Text Info/History Metrics Preview PDF Abstract Sleep-wakefulness regulation dynamically evolves along development in a wide range of organisms. While the mechanism regulating sleep in adults are relatively well understood, little is known about its counterpart in early developmental stages. Here we report a neuropeptidergic circuitry that modulates sleep in developing Drosophila larvae. Through an unbiased screen, we identified the neuropeptide Hugin and its receptor PK2-R1 as critical regulators of larval sleep. Our genetic and behavioral data suggest that HugPC neurons secrete Hugin peptides to activate Insulin-producing cells (IPCs), which expresses a Hugin receptor PK2-R1. IPCs, in turn, release insulin-like peptides (Dilps) to regulate sleep. We further show that the Hugin/PK2-R1 axis is dispensable for adult sleep. Our findings thus reveal the neuromodulatry circuit that regulates developmental sleep in larvae, and highlight differential impacts of the same modulatory axis on early-life sleep and adult sleep. Introduction Sleep is a fundamental state with significant impacts on many aspects of cognition and metabolism ( Blumberg et al., 2005 ; Davis et al., 2004 ; Sorribes et al., 2013 ). While sleep remains essential throughout life, its regulation dynamically unfolds over the course of development. For instance, the pace of sleep-wakefulness cycle is relatively fast in infants, while gradually slows down and stabilize in adults ( Blumberg et al., 2005 ; Davis et al., 2004 ; Sorribes et al., 2013 ). Furthermore, sleep-wake cycle in infants is typically independent of circadian rhythm, but it gradually comes under the circadian regulation ( Davis et al., 2004 ; Frank et al., 2017 ; Poe et al., 2023 ). Such developmental evolution of sleep-wakefulness cycles appears to be conserved across a wide range of organisms including flies, fishes, mammals and humans ( Blumberg et al., 2005 ; Davis et al., 2004 ; Poe et al., 2023 ; Sorribes et al., 2013 ; Szuperak et al., 2018a ). In contrast to deep insights into the molecular and neural mechanisms underlying adult sleep regulation ( Barlow & Rihel, 2017 ; Scammell et al., 2017 ; Shafer & Keene, 2021 ), little is known about regulatory mechanisms underlying developmental sleep in infants, partially due to a lack of convenient models to study developmental sleep. Drosophila larvae have recently emerged as a suitable model to study sleep regulation mechanisms. Recent studies have reported that the second instar larvae show short periods (< 6 seconds) of “inactive state” during locomotion. Importantly, such “inactive state” is consistent with the general definition of sleep: reduced responsiveness to noxious stimuli, homeostatic response to sleep deprivation, and rapid reversibility upon stimulation (Szuperak et al., 2018), suggesting that the “inactive state” in larvae likely corresponds to the sleep state. Interestingly, loss-of-function mutations in the clock genes clock and cyc fail to impact the larval sleep, while significantly altering the sleep patterns in adult flies ( Dubowy & Sehgal, 2017 ; Hendricks et al., 2003 ; Szuperak et al., 2018). Likewise, mutations in the dopamine transporter DAT fail to influence larval sleep, while reducing sleep amounts in adult ( Kume et al., 2005 ; Szuperak et al., 2018). These observations imply that sleep regulation mechanisms might be at least in part distinct between larvae and adults, yet the neural mechanism of larval sleep remains vastly understudied compared to that of adult sleep. In this study, we performed an unbiased genetic screen in the second instar larvae, and identified the neuropeptide Hugin and its receptor PK2-R1 as a pair critical for larval sleep. At the circuit level, Hugin-producing HugPC neurons directly stimulate the insulin-producing cells (IPCs) via PK2-R1 to regulate sleep. We further found that regulators of larval sleep are either irrelevant or exert the opposite effects on adult sleep. Overall, our findings uncover the neuropeptidergic circuitry that regulates developmental sleep in larvae, and highlight mechanistic differences between larval and adult sleep. Results PK2-R1 is required for wake/sleep control in Drosophila larvae To understand molecular and neural mechanisms of larval sleep, we modified a previously reported system ( Churgin et al., 2019 ; Szuperak et al., 2018) to automatically quantify the sleep amount of second instar larvae ( Figure 1-figure supplement 1A-1C ) (see Materials & Methods for details). Our system automatically annotates larvae in each frame as either “inactive” or “active” states on the basis of pixel changes, with ∼90% accuracy compared to manual annotation ( Figure 1-figure supplement 1C ; see Methods for details). We found that bouts of ≥ 12 inactive frames are consistent with the general criteria for sleep; reduced responsiveness to noxious stimuli, homeostatic response to sleep loss, and rapid reversibility upon stimulation ( Raizen et al., 2008 ; Szuperak et al., 2018) ( Figure 1-figure supplement 2B–D ). Therefore, the present study defines sleep as inactive state of ≥ 12 consecutive frames ( Figure 1-figure supplement 1A ). Download figure Open in new tab Figure 1—figure supplement 1. Detailed experimental setup for analyzing larval sleep (A) Schematic flow of automated larval sleep quantification. A second instar larva was placed in each of 24 wells, and all 24 wells were videotaped from above. For each well and frame, the image of larva was blurred and binarized to automatically locate the larva. Consecutive frames were then compared so that changes in pixel values were summed to estimate larval motion. This motion estimate was then used to classify every consecutive pair of frames as representing “active” or “inactive.” We found that inactivity across 12 consecutive frames meets the sleep criteria, as shown in Figure 1—figure supplement 2 . (B) Schematic representation of the 24-well chamber used for larval sleep assays. (C) Flow of automated processes of larval motion detection and active/inactive state determination. For each well and frame, the image of larva was blurred and binarized. Consecutive frames were then compared so that changes in pixel values were summed to estimate larval motion. This motion estimate was then used to classify every consecutive pair of frames as representing “active” or “inactive.” Download figure Open in new tab Figure 1—figure supplement 2. Larval “sleep state” in this study is consistent with behavioral criteria for sleep (A) Relationship between “inactive” state duration and spontaneous arousal probability. Sustained quiescence for ≥ 12 frames (open circles) significantly reduced arousal probability compared to 1-frame inactivity. * p < 0.05, NS: p ≥ 0.05 (chi-squared test). (B) The percentage of larvae that showed a response within the next 10 frames after high-intensity blue light. The number of events analyzed was 36 and 60 with and without blue light irradiation, respectively. * p < 0.05 (Fisher’s exact test). (C) Sleep amounts before, during, and after blue light irradiation. The number of larvae tested was 21 and 21 for the control and experimental groups, respectively. ** p < 0.01, NS: p ≥ 0.05 (Mann–Whitney U-test with Bonferroni correction). (D) Percentage of larvae that showed a response in the next 1 or 12 frames after blue light stimulation. Number of events analyzed: 1960 or 111 for 1- or 12-frame continuous inactivity prior to blue light illumination, respectively. **** p < 0.0001 (chi-squared test). Download figure Open in new tab Figure 1. PK2-R1 is required for larval sleep control (A) Sleep amounts in PK2-R1 or Oamb knockout mutants. Each dot represents an individual animal; in this and the following panels, “N” indicates the number of biologically independent animals per group, and the thick line and thin error bars indicate the median and interquartile range (Q1–Q3), respectively. *** p < 0.001 (Mann–Whitney U-test with Bonferroni correction). (B) Sleep amounts in larvae in which PK2-R1 neurons were silenced. *** p UAS-Kir::EGFP larvae. Using this system, we next carried out an unbiased screen for genes involved in sleep regulation. To this end, we tested 47 CRISPR knock-out lines of genes encoding enzymes or receptors for neuropeptides and monoamines ( Deng et al., 2019 )(Supplemental Table 1). As illustrated in Figure 1-figure supplement 3 , 13 out of 47 homozygous null mutants exhibited significant changes in sleep amounts compared to the control group. To further test the functions of these candidate genes, we next took advantage of the CRISPR knock-in drivers in which GAL4 sequence is inserted into each gene locus. With these drivers, we silenced neurons expressing each candidate gene by expressing the inward-rectifying potassium channel Kir2.1 ( Baines et al., 2001 ). Silencing neurons expressing Oamb and PK2-R1 resulted in significant sleep increase, phenocopying the knock-out of these genes ( Figure 1A and 1B and Figure 1-figure supplement 4D ). Oamb encodes the octopamine receptor and has been reported to be involved in larval sleep (Szuperak et al., 2018). The role of PK2-R1 in larval sleep, on the other hand, has been unknown to date. Given its strong expression in insulin-producing cells (Schlegel et al., 2016) and its function as a receptor for the neuropeptide Hugin, which modulates feeding ( Schoofs et al., 2014 ), we hypothesized that PK2-R1 might mediate neuropeptidergic signaling that links metabolic and sleep regulation during development. We thus focused on this gene as a candidate connecting behavioral and endocrine sleep control. Download figure Open in new tab Figure 1—figure supplement 3. CRISPR-knock-out screen for genes that regulate larval sleep. Total sleep amounts in CRISPR-knock-out mutants were measured for 18 h from the beginning of the second instar. The data for Control, PK2-R1, and Oamb mutants are identical to those shown in Figure 1 . Box plots are generated so that the center line indicates the median, the box limits indicate the upper and lower quartiles, and the whiskers indicate the minimum-to-maximum range. *** p < 0.005 (Kruskal–Wallis one-way ANOVA and post hoc Mann–Whitney U-test with Bonferroni correction). Number of larvae tested: 475 for the control and 10–52 for each mutant. Download figure Open in new tab Figure 1—figure supplement 4. Effects of neuronal manipulation on larval sleep Sleep amounts in larvae in which distinct neuronal populations were silenced. Kir2.1 expression was driven by each enhancer-GAL4 for “hit” genes identified in the CRISPR-knock-out screen. Each dot represents an individual animal in these panels, “N” indicates the number of biologically independent animals per group, and the thick line and thin error bars indicate the median and interquartile range (Q1–Q3), respectively. *** p < 0.001, ** p < 0.01, * p < 0.05, NS: ≥ 0.05 (Mann–Whitney U-test with Bonferroni correction) Insulin-producing cells express PK2-R1 and regulate sleep states in developing larvae Since the PK2-R1 driver broadly labeled neurons (∼1000 cells) throughout the CNS ( Figure 1C ), we next attempted to identify a subpopulation of these neurons relevant for sleep regulation. To narrow down the candidates within PK2-R1-positive populations, we examined subsets labeling GABAergic, cholinergic, or glutamatergic neurons, as well as those co-expressing other neuropeptides identified in our screen, but none of these manipulations reproduced the sleep phenotype caused by PK2-R1 perturbation. Notably, insulin-producing cells (IPCs) in the pars intercerebralis (PI), the neurosecretory center of Drosophila , have been reported to express PK2-R1 (Schlegel et al., 2016). Consistently, the PK2-R1 2A-LexA knock-in driver labeled all IPCs ( Figure 2A and B ). To assess whether IPCs are involved in larval sleep, we silenced IPCs with Kir2.1 expression, using the IPC-specific drivers Dilp3- and Dilp5-GAL4 ( Figure 2C ). We found that silencing of IPCs significantly increased the sleep amounts compared to the control, phenocopying PK2-R1 neuron silencing and PK2-R1 knockout ( Figure 1A ). Consistently, both Dilp3 and Dilp5 null mutants exhibited larger sleep amounts compared to the control ( Figure 2D ). These phenotypes of Dilp mutants and IPC silencing are unlikely to be accounted for by locomotion defects, as the travel distances during wake periods were unaffected ( Fig. 2—figure supplement 1 ). Through these trials, insulin-producing cells (IPCs) emerged as one of the populations whose silencing phenocopied PK2-R1 loss, supporting their key role in larval sleep regulation while not excluding contributions from other PK2-R1-expressing neurons. Collectively, these findings suggest that PK2-R1 in IPCs regulates larval sleep. Download figure Open in new tab Figure 1—figure supplement 5. Larval sleep phenotypes in PK2-R1 mutants quantified over 18 h and the first 6 h. (A–C) Sleep metrics quantified over an 18-hour period in second-instar larvae, whereas (D–F) show the same metrics quantified over the first 6 hours in the same animals. Total sleep is shown in (A) and (D), bout number in (B) and (E), and mean bout length in (C) and (F). Each dot represents an individual larva; the thick line indicates the median and the thin error bars indicate the interquartile range (Q1–Q3). Numbers in parentheses indicate N, the number of biologically independent animals per group. ** p < 0.01, * p < 0.05, NS: ≥ 0.05 (Mann–Whitney U-test with Bonferroni correction). Download figure Open in new tab Figure 2—figure supplement 1. Locomotion speeds are not consistently affected by genetical manipulations of PK2-R1 , IPCs, or Dilps (A) Larval locomotion speed in controls and PK2-R1 mutants. (B-C) Effects of silencing PK2-R1 neurons (B) and IPCs (C) on larval locomotion speed, shown together with the corresponding genetic controls. (D) Larval locomotion speed in Dilp3 or Dilp5 null mutants. Average locomotion speed during wake periods was calculated from the datasets shown in Figures 1A , 1B, 2C, and 2D. N indicates the number of biologically independent larvae per group. Each dot represents an individual larva; thick lines indicate the median and thin error bars indicate the interquartile range (Q1-Q3). *** p < 0.0001, * p < 0.05, NS: p ≥ 0.05 (Mann-Whitney U-test with Bonferroni correction). Download figure Open in new tab Figure 2. IPCs and Dilps negatively regulate larval sleep (A) Triple labeling of PK2-R1 neurons expressing nuclear-localized RFP (magenta), Dilp3 neurons expressing nuclear-localized GFP (green), and anti-Dilp2 positive cells (blue). Top panels show signals in the larval brain and the VNC. Bottom panels show magnified images of the white-squared area in each top panel, focusing on the dorsomedial brain region where the cell bodies of IPCs are located. Note that all IPCs labeled with Dilp3-GAL4 overlapped with PK2-R1 2A-LexA -expressing cells. Similar results were obtained from 5 independent samples of the same genotype. (B) Simultaneous detection of PK2-R1 neurons expressing nuclear-localized RFP (magenta), Dilp5 neurons expressing nuclear-localized GFP (green), and anti-Dilp2 positive cells (blue). Similar results were obtained from 5 independent samples of the same genotype. (C) Effect of IPCs silencing on larval sleep. Each dot represents an individual animal; in this and the following panels, “N” indicates the number of biologically independent animals per group, and the thick line and thin error bars indicate the median and interquartile range (Q1–Q3), respectively. *** p < 0.001, ** p < 0.01, * p < 0.05 (Mann–Whitney U-test with Bonferroni correction). (D) Sleep amounts in Dilp3 or Dilp5 null mutants. *** p < 0.001, ** p < 0.01 (Mann–Whitney U-test with Bonferroni correction). Hugin-expressing neurons control larval sleep states Given that PK2-R1 is a receptor of the neuropeptide Hugin ( Rosenkilde et al., 2003 ), we next examined whether Hugin is involved in the larval sleep. We found that Hug mutant larvae exhibited significantly larger sleep amounts compared to the control ( Figure 3A ), consistent with PK2-R1 knockouts ( Figure 1A ). When we silenced Hug-expressing neurons by driving the expression of Kir2.1 with the Hug CRISPR-knock-in driver Hug 2A-GAL4 ( Deng et al., 2019 ), it resulted in a significant increase of sleep amounts without detectable changes in locomotion speed ( Figure 3B and Figure 3—figure supplement 1B ). Conversely, activation of Hug -expressing neurons with the red-shifted channel rhodopsin ReaChR ( Inagaki et al., 2014 ) or heat-sensitive ion channel TrpA1 ( Hamada et al., 2008 ) both caused a significant reduction of sleep amount ( Figure 3C and D ) and increased locomotor activity. However, locomotion changes were not consistently observed upon either activation or suppression of Hug neurons ( Figure 3—figure supplement 1 ), suggesting that changes in sleep cannot be simply explained by locomotor alterations. We further quantified larval feeding using a dye-based ingestion assay and found that silencing HugPC neurons reduced food intake ( Figure 3—figure supplement 3 ), indicating that the sleep phenotype is unlikely to be explained by feeding behavior being misclassified as sleep. These data together indicate that Hug -expressing neurons are responsible for downregulating larval sleep. Download figure Open in new tab Figure 3—figure supplement 1. Effects of Hug pathway manipulations on larval locomotion speed. (A) Locomotion speed of Hug knockout mutants during wake periods. (B) Effects of silencing Hug neurons on locomotion speed. (C-D) Effects of activating Hug neurons on locomotion speed, shown as the light ON/light OFF ratio (C) and the absolute locomotion speed (D). (E) Effects of silencing HugPC neurons on locomotion speed. Locomotion speed was quantified from the same videos used for the larval sleep assays in Figure 3 . Each dot represents an individual larva; thick lines indicate the median and thin error bars indicate the interquartile range (Q1-Q3). N indicates the number of biologically independent animals per group. NS p ≥ 0.05 (Mann-Whitney U-test) in (A). *** p < 0.0001, * p < 0.05, NS p ≥ 0.05 (Mann-Whitney U-test with Bonferroni correction) in (B-E). Download figure Open in new tab Figure 3. Hug neurons negatively regulate larval sleep (A) Sleep amounts in Hug CRISPR-knockout mutant larvae. **** p < 0.0001 (Mann–Whitney U-test). Each dot represents an individual animal; in this and the following panels, “N” indicates the number of biologically independent animals per group, and the thick line and thin error bars indicate the median and interquartile range (Q1–Q3), respectively. (B) Effect of silencing Hug neurons on larval sleep amount. ** p < 0.01, * p < 0.05 (Mann–Whitney U-test with Bonferroni correction). (C) Larval sleep changes induced by optogenetic activation of Hug neurons. For each genotype, sleep duration in the 1-h light-ON period was normalized to that in the 1-h light-OFF phase. * p < 0.05 (Mann–Whitney U-test with Bonferroni correction). (D) Larval sleep during thermogenetic activation of Hug neurons. ** p < 0.01, * p < 0.05 (Mann–Whitney U-test with Bonferroni correction). (E) Visualization of HugPC neurons and IPCs labeled by rCD2::RFP (magenta) and mCD8::GFP (green), respectively. The bottom panels show magnified images of the white-squared dorsomedial region in the top panels, where HugPC neurons project their axons close to the cell bodies of the IPCs. While the top panels are z-stacks of 122 image slices (1 µm interval) covering the entire brain tissue, the bottom panels are projections of 60 slices centering around IPCs. Similar results were obtained from 5 independent samples of the same genotype. (F) The effect of neuronal silencing confined to the HugPC subpopulation. ** p < 0.01, * p < 0.05 (Mann–Whitney U-test with Bonferroni correction). Hug -expressing neurons are comprised of 20 cells in the subesophageal zone (SEZ) of the brain, and are classified into four subpopulations based on their projection patterns ( Bader et al., 2007 ). Of these, one subpopulation called HugPC neurons ( Bader et al., 2007 ) have been reported to form synaptic connections with IPCs ( Hückesfeld, Peters, & Pankratz, 2016 ). We thus suspected that HugPC neurons may be responsible for sleep regulation. In line with this notion, axons of HugPC neurons, labeled by a newly generated HugPC-LexA driver ( Figure 3—figure supplement 2 ), and IPCs, labeled by Dilp3-GAL4 ( Figure 3E ), exist in space close to each other. Indeed, silencing of HugPC neurons by Kir2.1 significantly increased larval sleep ( Figure 3F ), phenocopying those observed in Hugin and PK2-R1 mutants. These results hint at the possibility that HugPC neurons activate IPCs via Hugin/PK2-R1 signaling to regulate sleep. Download figure Open in new tab Figure 3—figure supplement 2. Generation of HugPC-LexA transgenic lines (A-D) Labeling patterns of four HugPC-LexA strains in larval CNS visualized by rCD2::RFP. Four transgenic lines were generated by injecting the same plasmid into the following landing sites individually: su(Hw)attP5, VK00005, attP2, and attP40. Magenta arrowheads represent cell bodies apparently located outside the previously classified HugPC subpopulation ( Bader et al., 2007 ). Hugin triggers Ca 2+ elevation and Dilp secretion in larval IPCs via PK2-R1 A previous report showed that IPCs exhibit increased firing rates and intracellular calcium (Ca 2+ ) levels upon stimulation ( Kréneisz et al., 2010 ). This motivated us to next test if Hugin activates IPCs. To further visualize the output pattern of HuginPC neurons, we expressed the presynaptic marker Syt-eGFP in HuginPC neurons ( Figure 4—figure supplement 1 ).To test whether Hugin activates IPCs, we utilized the recently developed calcium indicator CRTC::GFP, which can report Ca 2+ dynamics in the timescale of several minutes ( Bonheur et al., 2023 ). As glucose reportedly triggers Ca 2+ dynamics in IPCs in a range of minutes ( Oh et al., 2019 ), we wondered if CRTC::GFP can detect this dynamics, as a positive control. To this end, we applied D-glucose to isolated larval brains and found a significant cytosol-to-nuclear translocation of CRTC::GFP in IPCs within ∼5 min, confiming that this reporter can indeed detect Ca 2+ responses in IPCs upon stimulation ( Figure 5B ). We then examined whether Hug neurons act upstream of PK2-R1 -expressing IPCs by measuring Ca 2+ responses of IPCs while thermogenetically activating Hug neurons with TrpA1. We found that activation of Hug neurons tended to increase Ca 2+ levels in IPCs ( Figure 4B and C ), consistent with the HuginPC-to-IPCs axis ( Figure 4D ). Next, we tested whether Hug peptides can induce Ca 2+ responses in IPCs using ex vivo preparations. The Hug gene encodes a precursor that potentially produces at least two distinct peptides, Hug-γ and PK-2, both of which can bind PK2-R1 to induce Ca 2+ responses ( Rosenkilde et al., 2003 ). We thus incubated the CRTC::GFP-expressing larval brain with chemically synthesized Hug-γ or PK-2 ( Kréneisz et al., 2010 ; Oh et al., 2019 ) ( Figure 5A ). We found that bath application of either Hug-γ or PK-2 induced significant Ca 2+ responses in IPCs ( Figure 5B–D ). In agreement with the idea that Hug peptides activate IPCs via PK2-R1 , knock-out of PK2-R1 blocked the Hug peptide-evoked Ca 2+ elevations in IPCs ( Figure 5F and G ). Similar results were obtained when we bath-applied the synthetic Neuromedin U (NMU), the mammlaian homlog of Hugin ( Figure 5A and 5G ), suggesting the role of Hugin/PK2-R1 signaling may be conserved across species. Furthermore, Dilp3 accumulated in IPCs were significantly reduced after applying Hug peptides, suggesting that Hugin causes IPCs to release Dilp3 ( Figure 5E ). Collectively, these data suggest that Hug neurons activate IPCs via the Hug/PK2-R1 signaling to regulate sleep ( Figure 5H ). Download figure Open in new tab Figure 3—figure supplement 3. Silencing HugPC neurons reduces larval food intake measured by a dye-based assay. After a 30 min starvation, larvae were allowed to feed on red yeast paste placed on an apple juice agar plate, and food intake was quantified as the ratio of the red dye-stained body surface area to the total body surface area (ImageJ). Each dot represents an individual larva; the thick line indicates the median and the thin error bars indicate the interquartile range (Q1–Q3). ** p < 0.01 (Mann–Whitney U-test). Download figure Open in new tab Figure 4-figure supplement 1. Axonal projections of HuginPC neurons visualized with a presynaptic marker. Representative whole-mount image of the larval central nervous system showing Syt-eGFP signals expressed in HuginPC neurons. Scale bar, 100 µm. Download figure Open in new tab Figure 4. Activation of Hug neurons triggers Ca2+ responses in larval IPCs. (A) Schematic workflow for assessing intracellular Ca2+ levels in IPCs while thermogenetically activating Hug neurons. Larvae were exposed to low (18°C) or high (30°C) temperature for 1 h, followed by imaging of CRTC::GFP in IPCs and calculation of the CRTC::GFP nuclear localization index (NLI; see Methods). (B–C) Nuclear-localized CRTC::GFP signal (CRTC::GFP NLI) in larval IPCs under low-temperature (B) or high-temperature (C) conditions, with or without thermogenetic activation of Hug neurons. Each dot represents an individual cell; the thick line indicates the median and the thin error bars indicate the interquartile range (Q1–Q3). “N” indicates the number of cells analyzed per group. **** p < 0.0001, NS p ≥ 0.05 (Mann–Whitney U-test). (D) Working model of a neuronal network in which Hug neurons activate IPCs to regulate larval sleep Download figure Open in new tab Figure 5. Hug peptides induce Ca 2+ responses in larval IPCs via PK2-R1 (A) Schematic flow of peptide application followed by Ca 2+ imaging. (B–C) Ca 2+ responses in laval IPCs during ex vivo bath application of either glucose (B) or Hug peptides (C). In this and the following panels, ‘N’ indicates the number of cells used for each group, and the thick line and thin error bar represent the median and interquartile range (Q1–Q3), respectively. **** p < 0.0001, *** p < 0.001 (Mann–Whitney U-test with Bonferroni correction). (D) Representative images of larval IPCs upon peptide application. Scale bars, 2 μm. (E) Anti-Dilp3 signal intensity measured within the cytosolic areas of larval IPCs. *** p < 0.001, * p < 0.05 (Mann–Whitney U-test with Bonferroni correction). (F–G) Ca 2+ responses in larval IPCs derived from PK2-R1 knockout mutants, measured after either glucose (F) or Hug peptide application (G). ** p < 0.01, NS p ≥ 0.05 (Mann–Whitney U-test with Bonferroni correction). (H) A model where Hug peptides, but not glucose, activate IPCs via the PK2-R1 receptor in the larval brain. Hugin/PK2-R1 axis has distinct impacts on wake/sleep control in larvae and adults Given the differences between larval vs adult sleep in temporal pattern and circadian influence, we next wondered if Hug/PK2-R1/Dilps axis functions in adult sleep as well. As in larvae, PK2-R1 knockout adults showed increased sleep amount compared to the control ( Figure 6—figure supplement 1B ). In contrast, Hug knockout or silencing of HugPC neurons failed to influence adult sleep ( Figure 6A and 6B ). This suggests that, unlike larvae, Hug is dispensable for adult sleep. Interestingly, we observed that the expression patterns of PK2-R1 and Hug, and the morphology of HugPC neurons and IPCs, showed a broadly similar distribution in larvae and adults, although we did not directly track individual neurons across development ( Figure 6—figure supplement 2 ). This implies that the differential roles of Hug in larvae vs adults are likely due to physiological differences in HugPC neurons and/or IPCs. We thus examined how Hug peptides evoke Ca 2+ responses in adult IPCs and found that neither Hug-γ nor PK-2 could induce Ca 2+ responses in adult IPCs ( Figure 6D and 6E ), unlike in larvae. Although PK-2 treatment even led to a modest increase in Dilp3 immunoreactivity in adult IPCs ( Figure 6F ), the physiological significance of this effect remains unclear; at present, we consider it most likely that PK-2 acts on larval and adult IPCs in a different manner. These data indicate that Hug induces Ca 2+ responses and Dilps secretion in larval IPCs but not in adult IPCs. Surprisingly, we further found that Dilp3 or Dilp5 null mutations reduced sleep amounts in adults, consistent with previous reports (Cong et al., Sleep 2015) ( Figure 6C ), while in larvae, these mutations in fact increased the sleep amounts. These data unexpectedly reveal how the same set of neuronal circuits and molecular signaling therein can evolve over development in a complex manner. Download figure Open in new tab Figure 6. Distinct impacts of Hugin/PK2-R1 axis on wake/sleep control in larvae and adult (A) Total sleep amounts in Hug kno ckout mutant adults. NS p ≥ 0.05 (Mann–Whitney U-test). In these and the following panels, ‘N’ indicates the number of biologically independent animals used for each group, and the thick line and thin error bar represent the median and interquartile range (Q1–Q3), respectively. NS p ≥ 0.05 (Mann–Whitney U-test). (B) Sleep amounts of adults in which HugPC neurons were silenced. *** p < 0.001, NS p ≥ 0.05 (Mann–Whitney U-test with Bonferroni correction). (C) Sleep amounts of Dilp3 or Dilp5 null mutant adults. *** p < 0.001 (Mann–Whitney U-test with Bonferroni correction). (D) Schematic flow of peptide application experiments using adult brains. (E–F) Ca 2+ sponses (E) or anti-Dilp3 signal intensity (F) in adult IPCs after Hug peptide application. ** p < 0.01, NS p ≥ 0.05 (Mann–Whitney U-test with Bonferroni correction). Discussion In this study, we have identified the neuropeptide Hugin and its receptor PK2-R1 as a ligand/receptor pair critical for sleep regulation in Drosophila larvae. We have further shown that larval IPCs express PK2-R1, respond and release Dilp3 upon Hugin stimulation. Surprisingly, the Hugin/PK2-R1 axis is dispensable for sleep regulation in adults, even though the gene expressions as well as the circuit structure appeared to be conserved between larval and adult brains. Furthermore, Dilps appear to modulate sleep in opposite directions in larvae and adults. Our findings thus uncover the neuropeptidergic modulation circuitry that specifically regulates developmental sleep, and suggest divergent usage of the same molecules and circuitry for sleep modulation in larvae and adults. Hugin/PK2-R1/Dilps axis negatively regulates sleep in larvae The present study shows that the neuropeptide Hugin and its receptor PK2-R1 are required for sleep regulation in the early larval stage. This notion is supported by following lines of evidence. First, knock-out mutations in Hugin or PK2-R1 increased sleep amounts compared to the control ( Figure 1A and 3A ). Consistently, silencing a subpopulation of Hugin -expressing neurons, called HugPC neurons, or PK2-R1 neurons increased sleep amounts ( Figure 1B , 3B and 3F). Second, thermogenetic activation of Hugin neurons decreased sleep amounts ( Figure 3D ). Third, HugPC neurons project their axons to IPCs in the brain that express PK2-R1 receptors ( Figure 3E ). Fourth, Hugin peptides induced Ca 2+ elevation and promoted Dilp3 release from IPCs in a PK2-R1-dependent manner ( Figure 5 ). Last, Dilp3 null mutations increased sleep amounts ( Figure 2D ). Based on these data, we propose a model in which the Hugin/PK2-R1/Dilps axis along the HugPC-IPC circuitry modulates larval sleep ( Figure 7 ). Download figure Open in new tab Figure 7. A schematic model of the Hugin/PK2-R1/Dilps axis in larval and adult sleep Schematic summary comparing larvae and adults. In larvae, bath application of hugin peptides activates IPCs, and both hugin signaling (HugPC neurons/hugin peptides) and IPC output (including Dilp3) act to suppress larval sleep. In adults, hugin peptides do not activate IPCs (dashed arrow), whereas IPC/Dilp3 signaling promotes adult sleep. Examples of Hugin acting through PK2-R1 are prominent in the context of feeding regulation in larvae and adults ( Schoofs et al., 2014 ). For example, a recent study has suggested that Hugin regulates the timing of pupariation and body wall contraction during pupariation through PK2-R1 in PTTH neurons ( Ohhara et al., 2024 ). In the present study, we showed that HugPC in the larval brain modulates sleep ( Figure 3F ). A previous electron microscopic analysis suggested that larval HugPC neurons likely form synaptic connections to downstream neurons including IPCs ( Hückesfeld et al., 2021 ; Schlegel et al., 2016). We here showed that PK2-R1 is required for larval sleep control and that thermogenetic activation of Hug neurons is associated with Ca 2+ responses in PK2-R1-expressing IPCs in vivo ( Figure 4 ). Importantly, our ex vivo data indicated that Hugin peptide-induced Ca 2+ response in IPCs was largely abolished by PK2-R1 mutation ( Figure 5 ), indicating the critical role of the Hugin/PK2-R1 axis for the HugPC-mediated IPC activation. Together, we propose that the HugPC-IPCs circuity mobilizes Hugin/PK2-R1 signaling to selectively modulate larval sleep. Interestingly, our ex vivo data indicated that Neuromedin U (NMU), a mammalian ortholog of Hugin, can trigger Ca 2+ responses in larval IPCs in a PK2-R1-dependent fashion ( Figure 5 ). Given that NMU has been implicated in sleep regulation in fishes and mammals ( Ahnaou & Drinkenburg, 2011 ; Chiu et al., 2016 ), an analogous ligand/receptor pair might be involved in sleep modulation in mammals as well. In this study, activation of Hugin–PK2-R1 signaling by bath application of Hugin peptides led to a significant reduction in Dilp3 immunoreactivity in IPCs, whereas Dilp2 immunoreactivity did not show a decrease under these conditions, suggesting that Hugin–PK2-R1 activation promotes Dilp3 secretion without strongly enhancing Dilp2 release in our assay. One plausible explanation is that individual Dilps are stored in partially distinct vesicular pools within IPCs and are released through stimulus-specific mechanisms. Consistent with this idea, previous work has shown that Dilp release from IPCs can be selective, with Dilp2 and Dilp3 responding to distinct nutritional cues (Kim and Neufeld, 2015). Confocal analyses in that study further demonstrated that Dilp2 and Dilp3 segregate into different intracellular granules, supporting the notion that individual Dilps can be targeted to distinct secretory pathways within the same neurons. In light of these observations, the selective effect of Hugin–PK2-R1 activation on Dilp3, but not Dilp2, is readily interpreted as another example of non-uniform Dilp regulation. Rather than uniformly driving secretion of all IPC-derived Dilps, we propose that Hugin–PK2-R1 signaling preferentially mobilizes the Dilp3 secretory pathway. Such selective regulation is reminiscent of piecemeal degranulation in mammalian eosinophils and mast cells, as well as stimulus-dependent mobilization of specific vesicle pools in pancreatic β cells. In this model, different Dilps are stored in partially distinct vesicular pools and are mobilized by different upstream inputs. While ultrastructural approaches such as electron microscopy could provide further insight into the anatomical segregation of Dilp-containing vesicles, these analyses are technically beyond the scope of the present study, and we therefore highlight them as an important direction for future work. PK2-R1 knockout larvae exhibited both an increase in sleep and a reduction in locomotion speed. Because our locomotor metric is defined as travel distance per unit time during epochs classified as wake, the observed decrease in locomotion speed cannot be fully explained by the increased fraction of time spent in sleep. Nevertheless, we cannot exclude the possibility that part of the apparent sleep increase in the knockout reflects broader motor impairments in PK2-R1–expressing circuits. PK2-R1 expression is not confined to IPCs but is broadly distributed in the nervous system, including populations in the ventral nerve cord ( Figure 1C ), making it likely that PK2-R1–positive neurons outside IPCs contribute to locomotor control. Consistent with this idea, when we restricted our manipulations to IPCs, we observed robust changes in sleep without significant alterations in locomotor activity ( Figure 2—figure supplement 1 ). These findings suggest that IPCs are more directly involved in sleep regulation, whereas other PK2-R1–expressing neurons may predominantly influence locomotion. Dh44 neurons promote arousal in second-instar larvae ( Poe et al., 2023 ), raising the possibility that hugin signaling may influence sleep through Dh44 in addition to IPCs. Consistent with this idea, the hugin receptor PK2-R1 is expressed not only in IPCs but also in Dh44 and DMS neurons (Schlegel et al., 2016). Thus, an extension of our model is that HuginPC-derived hugin may act on multiple PK2-R1–positive targets (including IPCs and potentially Dh44/DMS neurons) to shape larval sleep. Future cell type-specific perturbations of PK2-R1 in Dh44 and DMS neurons will be important to test the contribution of these pathways. Divergent roles of insulin signaling in regulation of larval sleep and feeding IPCs in larvae respond to exogenously applied Hug peptides, both by exhibiting elevated Ca 2+ levels and decreases in intracellular Dilp3 storage ( Figure 5C–E ). In contrast to Dilp3, however, the amount of Dilp2 accumulated in IPCs remains unchanged following Hug peptide application ( Figure 5—figure supplement 1B ). On the other hand, Dilp2 accumulation in IPCs decreases after glucose treatment ( Figure 5—figure supplement 1A ) ( Oh et al., 2019 ). These results unexpectedly suggest that larval IPCs form parallel channels inside, one for Hug-Dilp3 and the other for glucose-Dilp2. Mechanistically, Hug acts directly on IPCs via the PK2-R1 receptor. As for glucose, on the other hand, glucose-sensing CN neurons in the brain take up glucose and subsequently secrete another neuropeptide to activate IPCs ( Oh et al., 2019 ). Such parallelism inside IPCs might provide insight into why insect species broadly express multiple insulin-like peptide genes, in some cases even as many as 40 genes in the silkworm Bombyx mori (Okamoto, 2021). Drosophila genome harbors eight Dilp genes (Nässel et al., 2015), of which Dilp2, Dilp3, and Dilp5 are considered nutrient-related. Nevertheless, mRNA expression or release patterns of these Dilps are not identical under different nutrient conditions (Géminard et al., 2009; Kauffman & DiAngelo, 2024; Nässel et al., 2015; Oh et al., 2019 ; Tomoatsu et al., 2002). Furthermore, these Dilps exhibit different binding affinities for the insulin receptor (InR), the secreted decoy of InR (SDR), the ecdysone-inducible gene L2 (Imp-L2) (Okamoto et al., 2013). These reports hint at the differential roles of different insulin-like peptides, but mechanistic insights remain largely lacking. Our data of larval IPCs forming parallel channels for Hug-Dilp3 and glucose-Dilp2 may provide a point of entry for tackling this mystery. Download figure Open in new tab Figure 5—figure supplement 1. D-glucose but not Hug peptide cause Dilp2 reduction in larval IPCs (A–B) Anti-Dilp2 signal intensity measured in larval IPCs after glucose (A) or Hug-γ application (B). In this and the following panels, ‘N’ indicates the number of cells used for each group, and the thick line and thin error bar represent the median and interquartile range (Q1–Q3), respectively. **** p < 0.0001, * p < 0.05 (Mann–Whitney U-test). (C) Anti-Dilp3 signal intensity measured within the cytosolic areas of larval IPCs. NS ≥ 0.05 (Mann–Whitney U-test). Differential impacts of the same neuropeptidergic modulations on larval and adult sleep Our data show that the Hugin/PK2-R1/Dilps axis likely downregulates sleep amounts in larvae. Despite the significant increase of sleep amount in Hugin mutant larvae, Hugin mutant adults showed sleep amounts comparable to wild-type control ( Figure 6A ), consistent with a previous report that Hugin is dispensable for control of baseline sleep levels ( Schwarz et al., 2021 ). According to our histological analyses, the Hugin/PK2-R1 expression patterns as well as the HugPC-IPCs circuitry appear to be largely conserved in adults ( Figure 3E and Figure 6—figure supplement 2 ). Unlike larval brain, however, Hugin peptides failed to trigger Ca 2+ responses in IPCs in adults ( Figure 6E ). It is thus possible that Hugin/PK2-R1 signaling along the HugPC-IPCs circuitry is suppressed in adults. IPCs in adults receive multiple positive and negative modulatory inputs through GPCRs including the metabotropic GABA B receptors ( Enell et al., 2010 ), which suppresses IPC activity and Dilp release in adult IPCs ( Enell et al., 2010 ). It is thus plausible that such negative modulatory inputs to IPCs in adults might counteract with the Hugin/PK2-R1 axis to suppress Dilp release. In addition, our data suggest that Dilps modulate sleep amount in the opposite directions in larvae and adults ( Figure 7 ). Comparing the expression levels and activities of GPCRs in larval and adult IPCs would be essential to better understand how same modulatory signals over the course of development come to exert differential impacts on sleep. Interestingly, Hugin in adults appears irrelevant for the baseline sleep amount but is required for homeostatic regulation of sleep ( Schwarz et al., 2021 ). Thus, testing if Hugin/PK2-R1 axis is involved in the homeostatic regulation of larval sleep, and how such a system compares to its adult counterpart, may further provide mechanistic insights into how homeostatic sleep regulation maturates over development. Download figure Open in new tab Figure 5—figure supplement 2. Representative images of Dilp3 immunoreactivity in larval IPCs after bath application of Hug-γ peptide. Larval IPCs were labeled with Dilp3>mCherry (top row), and endogenous Dilp3 was detected by anti-Dilp3 immunostaining (bottom row). Representative brains from buffer-only controls (Brain #1 and #2) and Hug-γ peptide application (Brain #3 and #4) are shown. Scale bars: 25 μm (Brains #1–3) and 10 μm (Brain #4). Download figure Open in new tab Figure 6—figure supplement 1. Sleep patterns of adult flies with genetical manipulations in the Hugin and insulin pathways (A) Experimental setup for monitoring adult sleep using the Drosophila Activity Monitor (DAM). (B) Total daily sleep duration in adult flies carrying CRISPR-knockout mutations in genes whose larval sleep was significantly altered in Figure 1B . Each dot represents an individual fly; the thick line indicates the median and the thin error bars indicate the interquartile range (Q1–Q3). *** p < 0.01, ** p < 0.05 (Kruskal–Wallis one-way ANOVA followed by post hoc Mann–Whitney U-test with Bonferroni correction). (C) Daytime (left) and nighttime (right) sleep amounts in flies in which HugPC neurons were silenced. (D) Daytime (left) and nighttime (right) sleep amounts in Dilp3 or Dilp5 null mutants. In (C) and (D), numbers in parentheses indicate N, the number of biologically independent animals per group. *** p < 0.001, ** p < 0.01, * p < 0.05, NS p ≥ 0.05 (Mann–Whitney U-test with Bonferroni correction). Download figure Open in new tab Figure 6—figure supplement 2. Morphologies of PK2-R1 neurons, HugPC neurons, and IPCs in the adult brain. (A) Dual labeling of PK2-R1 neurons and IPCs expressing nuclear-localized RFP (magenta) and GFP (green), respectively. The top panels show signals in the entire adult brain. The bottom panels show magnified images of the white-squared area in each corresponding top panel, focusing on the dorsomedial brain region where the cell bodies of IPCs are located. Similar results were obtained from 5 independent samples of the same genotype. (B) Visualization of HugPC neurons and IPCs labeled by mCD8::GFP (magenta) and the anti-Dilp3 antibody (green), respectively. Similar results were obtained from 4 independent samples of the same genotype. Download figure Open in new tab Figure 6—figure supplement 3. Adult sleep architecture and waking activity following genetic manipulations of the hugin and insulin pathways. (A) Bout number per day (left), mean bout length (middle), and mean beam crossings during wakefulness (right) in Hug<attP mutants. (B) The same metrics in flies with HugPC neuron silencing. (C) The same metrics in Dilp3[1] and Dilp5<1 null mutants. (D) Total daily sleep in flies with Dilp3-positive neuron silencing. Each dot represents an individual fly; the thick line indicates the median and the thin error bars indicate the interquartile range (Q1–Q3). Numbers in parentheses indicate N, the number of biologically independent animals per group. * p < 0.05, ** p < 0.01, *** p < 0.001, NS p ≥ 0.05 (Mann–Whitney U-test with Bonferroni correction). In summary, we identified the Hugin/PK2-R1/Dilps signaling along the HugPC/IPCs circuit as the molecular and circuitry basis for developmental sleep regulation. Our study begins to fill the knowledge gap in larval sleep regulation, and sheds light on mechanistic differences in sleep regulation between early developmental stages and adults. Materials and Methods Fly strains The following strains of Drosophila melanogaster were obtained from the Bloomington Drosophila Stock Center: iso31 (an isogenic w 1118 strain; BDSC# 5905), PK2-R1 attP (BDSC# 84563), PK2-R1 2A-GAL4 (BDSC# 84686), PK2-R1 2A-LexA (BDSC# 84431), Dilp3 1 (BDSC# 30882), Dilp5 1 (BDSC# 30884), Dilp3-GAL4 (BDSC# 52660), Dilp5-GAL4 (BDSC# 66007), Hug attP (BDSC# 84514), Hug 2A-GAL4 (BDSC# 84646), Hug 2A-LexA (BDSC# 84397), UAS-Kir2.1::EGFP (BDSC# 6596), UAS-Stinger, LexAop-tdTomato.nls (BDSC# 66680), UAS-mCD8::mCherry, UAS-CRTC::GFP (BDSC# 99657), UAS-ReaChR (BDSC# 53741), UAS-TrpA1 (BDSC# 26263), LexAop-ReaChR (BDSC# 53746), LexAop-rCD2::RFP-p10.UAS-mCD8::GFP-p10 (BDSC# 67093). w 1118 was from Y. Rao (Peking University); HugPC-GAL4 ( Hückesfeld et al., 2016 ) was from M. Pankratz (University of Bonn); LexAop-TrpA1 ( Burke et al., 2012 ) was from S. Waddel (University of Oxford). HugPC-LexA was created in this study (see the following section, “Generation of transgenic lines”). Detailed lists of fly genotypes can be found in the key resource table and Source Data files. General fly maintenance Flies were maintained in an incubator (PHCbi, model# MTR-554-PJ) set to 25°C with a humidity of over 60% and a 9AM:9PM light/dark cycle. After 10 d of rearing in bottles, old flies were replaced with freshly eclosed ones to maintain the strain ( Furusawa et al., 2023 ). Egg collection Agar substrates for egg collection were prepared by microwave-heating a mixture of 2.0 g sucrose (Fujifilm Wako Pure Chemicals, cat# 196-00015), 3.0 g agar (BD, cat# 214010), 2.5 mL apple juice (Dole), and Milli-Q water up to 100 mL. Melted agar was poured into Petri dishes with a diameter of 4 cm (approximately 3 mL per dish). Solidified agar was scratched with tweezers to make the surface rough. Agar plates were stored inside a sealed container at 4°C. Upon egg collection, yeast paste (dried yeast (Nippon Beet Sugar Co., Ltd.) kneaded with Milli-Q water at 40% (w/w)) was placed in the center of the agar plates to further promote oviposition. Larval staging First-instar larvae were collected 2 d after egg collection. Among them, second-instar larvae that molted within a 2-h window were used for video recording. Distinguishing between first- and second-instar larvae was based on morphological characteristics: while anterior spiracles were not evident in the first-instar, fist-shaped spiracles were observed in the second-instar ( Lakhotia and Ranganath, 2021 ). Larval sleep analysis Twenty-four-well silicone chambers for sleep analysis, described in Figure 1—figure supplement 1 , were made by pouring dimethylpolysiloxane (PDMS) into molds based on a previous study ( Churgin et al., 2019 ; Szuperak et al., 2018). As a substrate, 120 µL of a mixture of 3% agar and 2% sucrose was poured into each well. After the gel solidified, 10 µL per well of 60 mg/mL yeast suspension was applied to the surface of the gel. Larvae were placed individually into the wells, and a glass plate (Azone, cat# 1-4540-01, 5 mm × 200 mm × 200 mm) was positioned on top of the chamber to prevent larvae from escaping. The chamber was placed inside a dark box equipped with a ring-type infrared LED (CCS, cat# LDR2-132IR940-LA) and a camera (The Imaging Source, cat# DMK27BUP031), and recording was started immediately without acclimation. Images were captured through the IC Capture software (The Imaging Source, version 2.5) under the following conditions: MJPEG Compressor for codec, a frame rate of 0.87 fps, and an image size of Y800 (2592 × 1944). Locomotor activity was quantified from the same video recordings used for larval sleep analysis. Travel distance was calculated as the cumulative displacement of the larval centroid over time, and locomotion speed was defined as total travel distance divided by the total duration of wakefulness, considering only time bins classified as wake. To minimize the possibility that feeding behavior was misclassified as sleep, we adjusted our immobility threshold using video recordings in which larval feeding bouts, including relatively subtle episodes, were apparent, and configured the algorithm so that feeding-associated movements would generally be classified as wake rather than inactivity. During the development and routine use of the assay, we also inspected representative recordings across genotypes and did not observe obvious feeding-related abnormalities or gross locomotor defects under our HuginPC/IPC manipulations. Sleep deprivation Blue light irradiation was performed using a 455-nm LED (Thorlabs, cat# M455L4) with a collimating adapter (Thorlabs, cat# SM1U25-A) ( Omamiuda-Ishikawa et al., 2020 ; Yoshino et al., 2017 ; Yoshino et al., 2025 ). The irradiation intensity was set to the maximum scale using a T-cube LED driver (Thorlabs, cat# LEDD1B). Temporal control of LED illumination was achieved using the Arduino UNO microcontroller board (Arduino, RRID:SCR_017284). For sleep deprivation experiments, the first 1 h was recorded without LED illumination, followed by another 1 h consisting of repeated cycles of “LED ON for 90 s and OFF for 30 s”. Immunohistochemistry Antibody staining of larval neurons was carried out as previously reported ( Morikawa et al., 2011 ; Omamiuda-Ishikawa et al., 2020 ). Briefly, dissection in phosphate-buffered saline (PBS); fixation in 4% paraformaldehyde/PBS at room temperature for either 90 min in CRTC::GFP experiments or 30 min in the others; washing with 0.3% Triton X-100/PBS (hereafter referred to as PBST) for 10 min × 3; blocking in 5% normal goat serum (NGS)/PBST at room temperature for 30 min; primary antibody treatment at 4°C overnight; washing with PBST for 10 min × 3; secondary antibody treatment at 4°C overnight; washing with PBST for 10 min × 3; and mounting in VECTASHIELD medium (Vector Laboratories, cat# H-1000, RRID:AB_2336789). Reactions were performed either in Terasaki 96-well plates (stem Corp., cat# P96R37N) for adults or in 48-well culture plates (Corning, cat# 3548) for larvae. The following primary antibodies were diluted in 5% NGS/PBST: chicken polyclonal anti-GFP (1:1000, Aves Labs, cat# GFP-1020, RRID:AB_10000240), mouse monoclonal anti-mCherry (1:1000, Takara Bio, cat# 632543, RRID:AB_2307319), rabbit polyclonal anti-Dilp3 (1:250, ( Veenstra et al., 2008 )), rabbit polyclonal anti-Dilp2 (1:2000, ( Okamoto et al., 2012 )), mouse monoclonal anti-BRP (1:100, Developmental Studies Hybridoma Bank, clone nc82; RRID:AB_2314866), and rat monoclonal anti-histone H3 phospho S28 (1:1000, Abcam, cat# ab10543; RRID:AB_2295065). The following secondary antibodies were diluted in 5% NGS/PBST: goat anti-chicken Alexa Fluor 488 (1:200, Thermo Fisher Scientific, cat# A-11039, RRID:AB_2534096), goat anti-mouse Alexa Fluor 555 (1:200, Thermo Fisher Scientific, cat# A-21424, RRID:AB_141780), goat anti-rabbit Alexa Fluor 633 (1:200, Thermo Fisher Scientific, cat# A-21071, RRID:AB_2535732), and goat anti-rat Alexa Fluor 633 (1:200, Thermo Fisher Scientific, cat# A-21094; RRID:AB_2535749). Fluorescence images were obtained using a confocal microscope (Leica, TCS SP8) with 20× (Leica, HC PL APO CS2 20×/0.75 IMM) and 63× (Leica, HC PL APO CS2 63×/1.40 OIL) objectives. Images were captured at 512 × 512-pixel resolution with an interval of 1 µm. Z-stack images of the maximum projections were generated using the ImageJ v1.53t software ( Schneider et al., 2012 ); RRID:SCR_003070). Generation of transgenic lines HugPC-LexA lines were generated by ΦC31 integrase-mediated transgenesis, as previously described (Groth et al., n.d.). The 544-bp enhancer region of Hug , essentially the same as that used to generate HugPC-GAL4 ( Hückesfeld et al., 2016 ), was PCR-amplified from fly genomic DNA. The gel-purified amplicon was subjected to another round of PCR to add hangout sequences. The backbone suicide-vector pBPnlsLexA::p65Uw (RRID:Addgene_26230) was initially amplified by transforming the ccdB -resistant E. coli strain, One Shot™ ccdB Survival™ 2 T1 R competent cells (Thermo Fisher Scientific, cat# A10460). The PCR product and the vector were double-digested by Aat II (NEB, cat# R0117S) and Fse I (NEB, cat# R0588S), followed by ligation using In-Fusion HD Cloning Kit w/Cloning Enhancer (Takara Bio, cat# 639635). The integrity of the resulting plasmid was confirmed by DNA sequencing. Plasmid insertion was targeted to four attP sites (attP2, attP40, VK00005, and su(Hw)attP5) individually through ΦC31 integrase-mediated transgenesis by BestGene Inc. (Chino Hills, CA). Transformants were selected based on the eye color marker. Among the generated strains harboring HugPC-LexA in each insertion site, labeling patterns in the larval brain were checked by driving fluorescent marker expression under the control of LexAop ( Figure 3—figure supplement 2 ). As the HugPC-LexA inserted in attP2 closely resembled the labeling pattern of the original HugPC-GAL4 , it was used in further experiments. The following primers were used for cloning: Forward primer for the HugPC enhancer; 1 st round PCR against genomic DNA: 5’-AAGGGTTTGGTTTAATTTATTTATGTCATA-3’ Reverse primer for the HugPC enhancer; 1 st round PCR against the genomic DNA: 5’-GGACAACTGATGCCAGCAGC-3’ Forward primer for the HugPC enhancer with hangout sequences (underlined); 2 nd round PCR against the 1 st round amplicon: 5’-gaaaagtgccacctgacgtAAGGGTTTGGTTTAATTTATTTATGTCATA-3’ Reverse primer for the HugPC enhancer with hangout sequences (underlined); 2 nd round PCR against the 1 st round amplicon: 5’-cccgggcgagctcggccggGGACAACTGATGCCAGCAGC-3’ Food intake assay Food intake was measured using a dye-based feeding assay adapted from Hückesfeld et al. (2016) . Apple juice agar plates were prepared with a spot of red yeast paste in the center. After 30 min starvation, larvae were transferred onto the yeast paste and videotaped for 20 min. Larvae were then collected in a small cell strainer and rinsed with 60°C hot water to remove external dye, transferred to glass slides, and photographed. Images were analyzed in ImageJ by measuring the area of red-stained dye signal and the total body area, and food intake was quantified as the ratio of dye-stained area to total body area for each larva. Measurement of neuronal activity and Dilp3 accumulation in IPCs The GFP-tagged CRTC (CRTC::GFP) reporter was applied to quantify the activities of larval IPCs, essentially according to the previous report in adult flies ( Bonheur et al., 2023 ). Parental Dilp3-GAL4 and the reporter line harboring UAS-CRTC::GFP and UAS-mCD8::mCherry (BDSC# 99657) were crossed, and eggs were collected within an 8–10 h time window. Offspring larvae were reared for 4 d after egg laying (AEL) at 25°C to early third instar. Collected larvae were washed with water and starved on water-soaked paper for 12–14 h. For in vivo thermogenetic activation, larva-containing Petri dishes were warmed at 30°C for 60 min. For ex vivo thermogenetic experiments, larvae were dissected in calcium- and sugar-free HL3.1 buffer ( Nakamizo-Dojo et al., 2023 )) at 30°C, and each brain was incubated in the same buffer at 30°C for 5 min. For ex vivo glucose or peptide application assays, larvae were first dissected in the same buffer as above at room temperature, and brain samples were then treated with either 20 mM D-glucose or 1 mM of chemically synthesized peptides dissolved in HL3.1 buffer for 5 min. After incubation, samples were subjected to immunohistochemical analyses to visualize GFP, mCherry, and Dilp3 signals (see the section “Immunohistochemistry”). Image processing was performed using ImageJ v1.53t (( Schneider et al., 2012 ); RRID:SCR_003070). Nuclear ROIs and cell body outlines were drawn based on the mCD8::mCherry fluorescence insensitive to neuronal activity, and the mean intensities of CRTC::GFP signals within the nucleus (F nuc ) and the cytoplasm (F cyto = F whole cell - F nuc ) were quantified. NLI was calculated using the following formula: (F nuc - F cyto )/(F nuc + F cyto ). To assess Dilp3 accumulation in IPCs, the anti-Dilp3 signal intensity was measured within the cytosol of each cell, excluding the nucleus. Data from 4-14 Dilp3-positive IPCs per brain, collected from 3-7 larvae, were pooled for each genotype/treatment group. We attempted to record GCaMP-based calcium dynamics in Hugin neurons and IPCs, but did not pursue this approach further because it was low-throughput and a positive-control assay (glucose application) did not elicit the expected IPC calcium response in our hands. Chemical synthesis of Hug peptides Peptides with the following amino acid sequences were chemically synthesized using the Fmoc solid phase method, and their purity was assessed by HPLC to be over 95%. All procedures were carried out by Eurofins Genomics K.K. (Tokyo, Japan). Drosophila Hug-γ: Acetyl-QLQSNGEPAYRVRTPRL-CONH2 (17 a.a.) Drosophila PK-2: Acetyl-SVPFKPRL-CONH2 (8 a.a.) Human NMU: Acetyl-FRVDEEFQSPFASQSRGYFLFRPRN-CONH2 (25 a.a.) Sleep quantification in adult flies The following parts of the Drosophila activity monitor (DAM) system were purchased from TriKinetics Inc. (Waltham, MA): the Drosophila Activity Monitor (cat# DAM2), the power supply interface unit (cat# PSIU9), monitor tubes with an outside diameter of 5 mm and a length of 65 mm (cat# PGT5x65), and tube caps (cat# CAP5-Black). Newly eclosed adult flies were collected under brief CO 2 anesthesia and kept as groups of 10–15 in standard food vials. Males and females were kept separately to avoid mating. To minimize disturbance by noise and vibration, flies were placed inside a fan-less Peltier-type incubator (Mitsubishi Electric Engineering Co., Ltd., cat# SLC-25) set to 25°C, externally connected with 5-mm diameter LED lights (MY-CRAFT, Ltd., cat# 5W0601) and a timer device (Panasonic Corp., cat# WH3311WP) to generate a 9AM:9PM light/dark cycle. After 3 d of eclosion, each fly was gently aspirated into a Pyrex glass monitor tube, one end of which was stuffed with agar food (2% (w/w) agar, 5% (w/w) sucrose), and the other was plugged with a cotton string. The tubes were set to the monitor device and placed inside the same fan-less incubator. The DAM system was connected to an external PC installed with DAMSystem3 Data Collection Software (RRID:SCR_021809; available at https://trikinetics.com/ ) for data acquisition, and beam-crossing by each fly was counted for two successive days. Time windows longer than 5 min without beam-crossing were judged as sleep bouts, following the conventional criteria for adult fly sleep ( Paul J. Shaw, Chiara Cirelli, Ralph J. Greenspan, & Giulio Tononi, 2000 ). Statistical analysis Statistical analyses by Kruskal–Wallis one-way ANOVA, Mann–Whitney U-test, or chi-square test were performed using Prism 9.5.1 (GraphPad, RRID:SCR_002798). Bonferroni correction was applied for multiple comparisons. Asterisks (*) represent p-values as indicated within each figure legend. All data necessary to reproduce the figure panels and statistical analyses are available as Source Data files. Acknowledgements We thank Y. Rao, M. Pankratz, S. Waddel, and the Bloomington Drosophila Stock Center for fly stocks; J. A. Veenstra for the anti-Dilp3 antibody; T. Nishimura for the anti-Dilp2 antibody; Y. Nishizuka for valuable suggestions on writing code for automated larval detection and sleep quantification; H. Ito, N. Yamaji, M. Maetani, A. Ogasawara, E. Kato, T. Rikiishi, S. Ando, M. Hayashi, and S. Miyazaki for technical assistance and fly maintenance; and the members of Emoto Lab for critical comments and discussion. This work is supported by MEXT Grants-in-Aid for Scientific Research on Innovative Areas “Dynamic regulation of brain function by Scrap and Build system” (KAKENHI 16H06456), JSPS (KAKENHI 16H02504), WPI-IRCN, AMED-CREST (JP22gm310010), JSTCREST, Toray Foundation, Naito Foundation, Takeda Science Foundation, and Uehara Memorial Foundation to K.E.; the Leading Initiative for Excellent Young Researchers (LEADER) from MEXT, JSPS (KAKENHI 22K06309), and AMED-PRIME (JP22gm6510011) to K.I.; Grant-in-Aid for JSPS Fellows (24KJ0911) to M.M.; and Grant-in-Aid for JSPS Fellows (22J21096; 22KJ1042) to C.H. Funder Information Declared Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan , KAKENHI 16H06456 , KAKENHI 16H02504 , The Leading Initiative for Excellent Young Researchers (LEADER) , KAKENHI 22K06309 Grant-in-Aid for JSPS Fellows, Japan , 24KJ0911 , 22J21096; 22KJ1042 WPI-IRCN, Japan Japan Agency for Medical Research and Development (AMED), Japan , JP22gm310010 , JP22gm6510011 Japan Science and Technology Agency (JST), Japan Toray Foundation, Japan Naito Foundation, Japan Takeda Science Foundation, Japan Uehara Memorial Foundation, Japan Footnotes ↵ $ Lead contact: emoto{at}bs.s.u-tokyo.ac.jp data presentation (use of dot-and-whisker plots instead of box-and-whisker plots), expanded analyses of larval sleep (bout number, bout length, and activity), additional control experiments, expanded methodological explanation, and strengthened discussion of selective insulin secretion and developmental differences. References ↵ Ahnaou , A. , & Drinkenburg , W. H. I. 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Share Neuropeptidergic circuit modulation of developmental sleep in Drosophila Chikayo Hemmi , Kenichi Ishii , Mana Motoyoshi , Masato Tsuji , Kazuo Emoto bioRxiv 2025.01.20.633944; doi: https://doi.org/10.1101/2025.01.20.633944 Share This Article: Copy Citation Tools Neuropeptidergic circuit modulation of developmental sleep in Drosophila Chikayo Hemmi , Kenichi Ishii , Mana Motoyoshi , Masato Tsuji , Kazuo Emoto bioRxiv 2025.01.20.633944; doi: https://doi.org/10.1101/2025.01.20.633944 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neuroscience Subject Areas All Articles Animal Behavior and Cognition (7633) Biochemistry (17681) Bioengineering (13890) Bioinformatics (41930) Biophysics (21446) Cancer Biology (18586) Cell Biology (25493) Clinical Trials (138) Developmental Biology (13374) Ecology (19897) Epidemiology (2067) Evolutionary Biology (24308) Genetics (15607) Genomics (22498) Immunology (17736) Microbiology (40385) Molecular Biology (17175) Neuroscience (88584) Paleontology (666) Pathology (2831) Pharmacology and Toxicology (4823) Physiology (7641) Plant Biology (15149) Scientific Communication and Education (2045) Synthetic Biology (4293) Systems Biology (9823) Zoology (2271)